Multi-Scale Fusion Methodologies for Head and Neck Tumor Segmentation
Head and Neck (H\&N) organ-at-risk (OAR) and tumor segmentations are essential components of radiation therapy planning. The varying anatomic locations and dimensions of H\&N nodal Gross Tumor Volumes (GTVn) and H\&N primary gross tumor volume (GTVp) are difficult to obtain due to lack of accurate and reliable delineation methods. The downstream effect of incorrect segmentation can result in unnecessary irradiation of normal organs. Towards a fully automated radiation therapy planning algorithm, we explore the efficacy of multi-scale fusion based deep learning architectures for accurately segmenting H\&N tumors from medical scans.
Code (0)
등록된 구현이 없습니다.
Tasks
Tumor SegmentationSimilar Papers 제목 키워드 기반
Fourier Angle Alignment for Oriented Object Detection in Remote Sensing
In remote sensing rotated object detection, mainstream methods suffer from two bottlenecks, directional incoherence at detector neck and task conflict at detecting head. Ulitising fourier rotation equivariance, we introd…
Object DetectionDual-Strategy Improvement of YOLOv11n for Multi-Scale Object Detection in Remote Sensing Images
Satellite remote sensing images pose significant challenges for object detection due to their high resolution, complex scenes, and large variations in target scales. To address the insufficient detection accuracy of the …
Object DetectionA Lightweight Multi-Scale Attention Framework for Real-Time Spinal Endoscopic Instance Segmentation
Real-time instance segmentation for spinal endoscopy is important for identifying and protecting critical anatomy during surgery, but it is difficult because of the narrow field of view, specular highlights, smoke/bleedi…
Real-time Instance SegmentationRethinking Features-Fused-Pyramid-Neck for Object Detection
Multi-head detectors typically employ a features-fused-pyramid-neck for multi-scale detection and are widely adopted in the industry. However, this approach faces feature misalignment when representations from different …
object-detectionObject DetectionA hierarchical fusion framework integrating random projection-based classifiers: application in head and neck squamous carcinoma cancer
Ensemble methods achieves better performance than single classifier model. Classifier diversity and fusion architecture are equally important for building a successful multi-classifier system. In this study, we introduce…
Decision MakingDiversitySurvival Prediction